Artificial intelligence in the boardroom

Being an advocate and guide for AI risk management means, in part, asking the right questions. This necessitates AI literacy. To take part in AI risk management, board members can build AI literacy through traditional methods, such as bringing in speakers and subject matter specialists and pursuing independent learning through classes, lectures, and reading.

A large language model (LLM) could also help in this regard, as an LLM-enabled application can summarize and help explain, in natural language, the complexities of AI’s functions, limits, and capabilities.

If AI literacy in the boardroom is important, even more so is fluency in the C-suite. Board members are in a position to urge executives to build their AI fluency. As the power and allure of AI grows and use cases multiply, business leaders need knowledge and familiarity with the technology to responsibly shape AI programs.

Decisions around AI safety, security, accountability, and all the factors that impact AI risk management flow out of a baseline understanding of what AI is and what it can do. As stewards of the enterprise, board members can encourage the importance of AI fluency.

Board members often hail from fields steeped in finance and business management. This background allows them to be informed leaders on fiscal and competitiveness issues. Given that AI is a technical and complex field raising its own collection of hurdles and risks, boards may look to expand their in-house subject matter knowledge by recruiting an AI professional to the board.

Such a person should bring experience as an operational AI leader with a track record of implementing successful AI projects in similar organizations. A professional with operational AI experience can provide the insight boards need for oversight and governance.

Governance isn’t an ad hoc exercise, and boards face the need to implement controls to guide the responsible use of AI. Boards may stand up subcommittees to oversee vital enterprise activities, such as for audits, succession planning, and risk management related to finance and operations. AI governance can be supported with a 
similar tactic.

The lexicon, capabilities, risks, and trajectory of AI are all in flux as the technology matures. A subcommittee or dedicated group is positioned to remain focused and informed on this complex, fast-changing technology. Boards could also extend existing subcommittees’ mandates to include AI components. For example, the audit committee could include planning for algorithmic auditing.

Given their role, board members aren’t directly working with AI, but they are important stakeholders with essential responsibilities. As enterprise leadership and lines of business explore how AI can be a productivity enhancer and innovation driver, the board can take a higher-level, big-picture view of AI programs and focus on guiding the enterprise in the responsible deployment and management of AI.

In this, it’s helpful to leverage a framework for assessing risk and how it impacts compliance and governance (for example, Deloitte’s Technology Trust Ethics framework). It can help board members make clear-eyed evaluations and usher the organization toward the most valuable use of AI.